本文基于三维离散余弦变换（3D-DCT）技术和扩频通信技术提出三维体数据鲁棒水印嵌入算法。
This paper proposes a robust watermarking algorithm for volume data based on three-dimension discrete cosine transform(3D-DCT) and spread-spectrum communication technique.
文章阐述了以小层精细标定、相干数据体、三维可视化等地震解释新技术为技术保证，地质、测井、地震综合解释为技术路线的构造研究方法及其应用。
A method for research of structure with a new seismic interpretation involving fine subzone calibration, coherent data volume and visualization as a technical guarantee and.
介绍了利用地震属性分析技术预测三维拟声波测井曲线数据体的方法。
The seismic multi-attribute analysis technology to predict 3D pseudo-sonic logging datum was introduced.
由于体绘制技术是将三维的离散数据直接转换为二维图像而不必生成中间几何图元，所以又称为直接体绘制。
Volume rendering is also known as direct volume rendering, for no intermediate geometry primitives are generated during its visualization process.
利用机载三维遥感技术能够同步获取三维位置和光谱数据的一体化信息。
The integrated information of 3d position and spectral data can be acquired simultaneously with the airborne 3d remote sensing technology.
医学体数据三维可视化技术有着广泛的应用前景。
The 3-d Visualization technique on medical volume data has extensive application and broad prospects.
改进传统窗口调节函数，结合模糊集理论和三维区域生长技术，提出并实现一种采用CT体数据场的人体器官提取新方法。
Based on the adjustment function, combining to fuzzy sets theory and 3d region growing technology, an approach for organ extraction from the ct human volumetric data was developed.
体绘制技术可以显示工业CT三维数据的整体特征和内部细节信息。
It can display the whole characteristics and inner detail information of ICT 3d data by volume rendering.
而图像的配准、图像分割、体数据集的构建、三维空间插值则是医学图像三维可视化实现过程中的关键技术环节。
The image registering, image segmentation, pixel data set construction and 3d special interpolation are the key technologies in medical images 3d reconstruction.
该项技术不仅提高了用相干切片解释断层的精度，而且提高了断层解释效率，使相干数据体技术在三维解释中的作用得到更大的发挥。
This technique improves the efficiency and accuracy of fault interpretation and make the coherence technique play a important role in 3D data interpretation.
为提高三维医学数据场的分割效率和准确率，本文利用特征聚类技术，提出了一种新的基于改进K - means聚类的三维医学数据场的体分割算法。
A clustering segmentation algorithm based on an improved K-means clustering method is used to improve the efficiency and accuracy of 3d medical image segmentation.
为提高三维医学数据场的分割效率和准确率，本文利用特征聚类技术，提出了一种新的基于改进K - means聚类的三维医学数据场的体分割算法。
A clustering segmentation algorithm based on an improved K-means clustering method is used to improve the efficiency and accuracy of 3d medical image segmentation.
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